What is Deepseek V3? Detailed Guide With Demo.

DeepSeek V3 is an advanced open-source large language model (LLM) developed by a Chinese AI firm known as DeepSeek. It was designed to rival the leading models like GPT-3.5 and GPT-4 that have been all the rage for the past couple of years. DeepSeek V3 boasts 671 billion parameters, which enables it to perform a wide array of tasks with high proficiency. Some even say it’s like ChatGPT but better, you will have to work it to know it for yourself.

Key Features

DeepSeek V3 has two standout features. One, it offers fast inference speed which makes it perfect for doing real-time tasks like instant responses or live data processing. Two, it is highly versatile and can perform pretty great in areas like math, coding, logical reasoning, and even handling multiple languages. It consistently scores high in benchmark tests, proving its reliability and accuracy. Whether you need quick solutions or advanced problem-solving, DeepSeek V3 is here to deliver top-notch performance.

Let’s learn more about how you can start using DeepSeek V3 right now. And it’s pretty easy too; just read ahead.

How to Get Started with DeepSeek-V3

The third version of Deepseek LLM is by far the best open-source LLM according to the Matrix and the model is free to use. So let’s check it out and see how you can use it. There are two main ways to use the model.

Method 1: Using the Chat UI

The easiest way to interact with DeepSeek-V3 is through its chat interface:

Visit the Official Website: Simply search for DeepSeek on your search engine and open the link to their official website (←or just use this link). Now click on “Start Now” for free access.

You will be directed to a login/signup page, where you will need to fill in your details. When you have successfully logged in, you will now see the chat window (like the one shown below).

Interact via Chat Window: The chat interface is user-friendly and pretty similar to ChatGPT. It also includes additional features like web search, a “Deep Think” option, and file attachment support.

Verify the Model: You can confirm you’re using DeepSeek-V3 by asking the model directly, ensuring you’re accessing their latest technology.

Method 2: Running the Model Locally via Hugging Face

If you don’t want to use the chat UI but wish to directly use the model, don’t worry, we got you. For this method you have to switch to Hugging Face. DeepSeek-V3 is available on Hugging Face with its weights fully released:

Clone the GitHub Repository: Start by cloning the DeepSeek AI GitHub repository:
git clone https://github.com/DeepSeekAI/DeepSeek-V3.git

Install Dependencies: Navigate to the repository and install the required dependencies:
pip install -r requirements.txt

Download Checkpoints: Download the Hugging Face checkpoints from the provided link on the GitHub page.

Run the Model Locally: Follow the instructions on the repository to set up and run the model. For systems with limited hardware, convert the model into a quantized version to reduce memory requirements.

Method 3 (sort of): API Integration

If you have an application in which you want to integrate the DeepSeek V3 as the LLM, you can do that through their API service. Let’s talk about how you can do that.

Obtain an API Key: Go to the DeepSeek’s API Platform page and sign up for an account. After you have successfully created your account, log in, and navigate to the “API Keys” section to create and manage your API keys.

Configure Your Environment:

• Base URL: Use https://api.deepseek.com as the base URL for API requests.
• API Key: Include your API key in the request headers for authentication.

Making API Requests: DeepSeek V3 supports various functionalities, including multi-round conversations, function calling, and JSON output. To send a chat request, use the following code:

import requests
import json

url = "https://api.deepseek.com/chat/completions"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer YOUR_API_KEY"
}
payload = {
    "model": "deepseek-chat",
    "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ],
    "stream": False
}

response = requests.post(url, headers=headers, data=json.dumps(payload))
print(response.json())

Remember to replace the placeholder text <YOUR-API-KEY> with your actual API key that you got when you created your account on the DeepSeek platform.

Note: It’s not a good idea to hardcode API keys directly into your code, especially if you plan on uploading your code to a version control platform like GitHub. If anyone with malicious intent gets ahold of your API key they might misuse it. Instead, store it in environment variables.

If you want more detailed information on available models and their capabilities to see which one best fits your use cases, you can go through DeepSeek’s API Documentation. And to monitor the status of the API and stay informed about any updates like any change in API key policy, you can check the API Status Page.

Key Considerations

DeepSeek V3 is massive, with 671 billion parameters, making it challenging to run on consumer-grade hardware. For those without high-performance systems, using the chat UI or waiting for Hugging Face spaces to support the model is recommended.

Why Try DeepSeek V3?

DeepSeek V3 has demonstrated performance rivaling GPT-4 and outperforming several open-source LLMs, including Claud 3.5 Sonnet. Whether you’re leveraging the model through the chat UI or deploying it locally, this state-of-the-art LLM delivers exceptional results across a wide range of applications.

The table below compares the key features of DeepSeek V3, GPT-4, and Claude 3.5 Sonnet.

If you want an in-depth analysis of DeepSeek models and where it stands when compared to other popular LLMs, we got you.

Metaschool DeepSeek Model Comparison

DeepSeek-Coder-V2: This model, an iteration of DeepSeek-Chat, is tailored for conversational interactions. It boasts an Artificial Analysis Quality Index of 67, indicating superior performance compared to average models. With an output speed of 16 tokens per second and a context window of 128k tokens, it offers efficient processing capabilities. Additionally, its pricing is competitive at $0.20 per 1 million tokens.

DeepSeek-V2.5: An instruction-tuned Qwen2.5 model with 72 billion parameters, DeepSeek-V2.5 focuses on advanced task completion. It has an Artificial Analysis Quality Index of 66 and an output speed of 13 tokens per second. The model also features a 128k token context window and is priced at $1.10 per 1 million tokens.

These pages include a comprehensive comparison of these models, which includes important metrics like quality, speed, pricing, and latency. These resources provide valuable insights to help you select the model that best aligns with your project requirements.

Demo

As promised, let’s quickly see the DeepSeek V3 in action. We will test some common tasks that we usually use tools like ChatGPT for.

Generating Code

Fixing Code

Web Search

Generating Text

Related Reading:

• What is LLM in AI? Large Language Models Explained!
• HumanEval Benchmark: Evaluating LLM Code Generation Capability
• StarCoder: LLM for Code — A Comprehensive Guide
• What is LangChain: Complete Guide 2025

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